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from diffusers import DiffusionPipeline, EulerDiscreteScheduler
import torch
from transformers import pipeline
import gradio as gr
from PIL import Image
from diffusers.utils import load_image
import os, random, gc, re, json, time, shutil, glob
import PIL.Image
import tqdm
from accelerate import Accelerator
from huggingface_hub import HfApi, InferenceClient, ModelCard, RepoCard, upload_folder, hf_hub_download, HfFileSystem
HfApi=HfApi()
HF_TOKEN=os.getenv("HF_TOKEN")
HF_HUB_DISABLE_TELEMETRY=1
DO_NOT_TRACK=1
HF_HUB_ENABLE_HF_TRANSFER=0
accelerator = Accelerator(cpu=True)
InferenceClient=InferenceClient()
apol=[]
pipe = accelerator.prepare(DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.bfloat16, use_safetensors=True, variant="fp16", safety_checker=None))
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.unet.to(memory_format=torch.channels_last)
pipe = accelerator.prepare(pipe.to("cpu"))
def chdr(apol,prompt,modil,stips,fnamo,gaul):
try:
type="SDXL_Trb"
los=""
tre='./tmpo/'+fnamo+'.json'
tra='./tmpo/'+fnamo+'_0.png'
trm='./tmpo/'+fnamo+'_1.png'
flng=["yssup", "sllab", "stsaerb", "sinep", "selppin", "ssa", "tnuc", "mub", "kcoc", "kcid", "anigav", "dekan", "edun", "slatineg", "xes", "nrop", "stit", "ttub", "bojwolb", "noitartenep", "kcuf", "kcus", "kcil", "elttil", "gnuoy", "thgit", "lrig", "etitep", "dlihc", "yxes"]
flng=[itm[::-1] for itm in flng]
ptn = r"\b" + r"\b|\b".join(flng) + r"\b"
if re.search(ptn, prompt, re.IGNORECASE):
print("onon buddy")
else:
dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type}
with open(tre, 'w') as f:
json.dump(dobj, f)
HfApi.upload_folder(repo_id="JoPmt/hf_community_images",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type,'haed':gaul,}
try:
for pxn in glob.glob('./tmpo/*.png'):
os.remove(pxn)
except:
print("lou")
with open(tre, 'w') as f:
json.dump(dobj, f)
HfApi.upload_folder(repo_id="JoPmt/Tst_datast_imgs",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
try:
for pgn in glob.glob('./tmpo/*.png'):
os.remove(pgn)
for jgn in glob.glob('./tmpo/*.json'):
os.remove(jgn)
del tre
del tra
del trm
except:
print("cant")
except:
print("failed to make obj")
def plax(gaul,req: gr.Request):
gaul=str(req.headers)
return gaul
def plex(prompt,stips,nut,wit,het,gaul,progress=gr.Progress(track_tqdm=True)):
gc.collect()
apol=[]
modil="stabilityai/sdxl-turbo"
fnamo=""+str(int(time.time()))+""
if nut == 0:
nm = random.randint(1, 2147483616)
while nm % 32 != 0:
nm = random.randint(1, 2147483616)
else:
nm=nut
generator = torch.Generator(device="cpu").manual_seed(nm)
image = pipe(prompt=[prompt]*2,num_inference_steps=stips,width=512,height=512,guidance_scale=0.0,generator=generator)
for a, imze in enumerate(image["images"]):
apol.append(imze)
imze.save('./tmpo/'+fnamo+'_'+str(a)+'.png', 'PNG')
chdr(apol,prompt,modil,stips,fnamo,gaul)
return apol
def aip(ill,api_name="/run"):
return
def pit(ill,api_name="/predict"):
return
with gr.Blocks(theme=random.choice([gr.themes.Monochrome(),gr.themes.Base.from_hub("gradio/seafoam"),gr.themes.Base.from_hub("freddyaboulton/dracula_revamped"),gr.themes.Glass(),gr.themes.Base(),]),analytics_enabled=False) as iface:
##iface.description="Running on cpu, very slow! by JoPmt."
out=gr.Gallery(label="Generated Output Image", columns=1)
inut=gr.Textbox(label="Prompt")
gaul=gr.Textbox(visible=False)
btn=gr.Button("GENERATE")
with gr.Accordion("Advanced Settings", open=False):
inyt=gr.Slider(label="Num inference steps",minimum=2,step=1,maximum=10,value=4)
indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)
inwt=gr.Slider(label="Width",minimum=256,step=32,maximum=768,value=512)
inht=gr.Slider(label="Height",minimum=256,step=32,maximum=768,value=512)
btn.click(fn=plax,inputs=gaul,outputs=gaul).then(fn=plex, outputs=[out], inputs=[inut,inyt,indt,inwt,inht,gaul])
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=20,inline=False,show_api=False) |